What type of analysis is used to compare more than two means in a within-subjects design?
Explanation
This question tests the knowledge of the correct statistical tool for a specific research design: a within-subjects study with three or more conditions. The text distinguishes this from other types of ANOVA.
Other questions
What is the primary purpose of null hypothesis testing in research?
What are the two primary considerations that determine the p-value in null hypothesis testing?
Under what condition can a weak relationship still be considered statistically significant?
Which statistical test is most appropriate for comparing the mean score of a single sample to a known or hypothetical population mean?
A researcher is conducting a study with a within-subjects design, measuring each participant's performance before and after a training intervention. Which test should be used to compare the mean scores?
For which of the following scenarios would a one-way ANOVA be the most appropriate null hypothesis test?
What does it mean if a researcher commits a Type I error?
A researcher fails to find a significant effect of a new drug, concluding it is ineffective. However, the drug does have a real, albeit small, effect in the population. What kind of error has the researcher made?
What is the statistical power of a research design?
Which of the following is a criticism of null hypothesis testing mentioned in the text?
What is the 'replicability crisis' in psychology referring to?
What has been a primary response to the 'replicability crisis' in psychology?
In a memory experiment, the mean scores for participants in Condition A and Condition B were exactly the same. What can be concluded about the statistical significance of this result?
A student finds a correlation of r = .04 between the number of university units students are taking and their level of stress. What is the most likely conclusion about this finding?
A researcher is studying the effectiveness of two forms of psychotherapy for social phobia using an independent-samples t-test. What would it mean for the researcher to commit a Type II error in this context?
When explaining a p-value of .02 to someone unfamiliar with statistics, what is the correct explanation that avoids common misinterpretations?
What is the key purpose of open science practices like pre-registration of hypotheses and sharing raw data?
A null hypothesis test of Pearson's r is used to compare a sample correlation coefficient to what hypothetical population value?
Which type of ANOVA is used for research with factorial designs, where there is more than one independent variable?
What is the relationship between statistical power and a Type II error?
The logic of null hypothesis testing begins with assuming the null hypothesis is true. What is the next step in the process?
What does the text suggest should accompany every null hypothesis test to provide a more complete picture of the research finding?
A confidence interval is described as a range of values computed in such a way that for a certain percentage of the time (usually 95 percent), the population parameter will lie within that range. What is an advantage of using confidence intervals over null hypothesis tests?
A researcher conducts a study comparing men and women on a psychological characteristic. The total sample size is 22 (12 women, 10 men). The effect size (Cohen's d) for the difference is found to be 0.2. Based on general principles, is this result likely to be statistically significant?
What is the primary reason researchers should ensure their studies have adequate statistical power BEFORE conducting them?
What does the practice of 'p-hacking' involve?
In a study, a researcher compares two means in a between-subjects design. Which statistical test is most commonly used for this purpose?
Which of the following describes the 'file drawer problem'?
A researcher finds that a new teaching method results in a statistically significant improvement in test scores (p less than .05). However, the average improvement is only half a point on a 100-point scale. This finding best illustrates the difference between what two concepts?
If a researcher rejects a true null hypothesis, what has occurred?
If a researcher fails to reject a false null hypothesis, what has occurred?
What is a major way to increase the statistical power of a study?
In a one-sample t-test example from the text, a health psychologist studied estimates of calories in a cookie. The actual number of calories was 250. The analysis resulted in t(9) = -3.07 and p = .013. What was the correct conclusion?
A one-way ANOVA was conducted to compare the calorie estimates of psychology majors, nutrition majors, and professional dieticians. The result was F(2, 21) = 9.92, p = .0009. What is the appropriate conclusion?
After finding a significant result in a one-way ANOVA with three or more groups, what is the purpose of conducting post hoc comparisons?
Why do researchers use modified t-test procedures like the Bonferroni test for post hoc comparisons instead of standard t-tests?
How does a repeated-measures ANOVA differ from a one-way ANOVA in its calculation?
What does a factorial ANOVA produce that a one-way ANOVA does not?
In an independent-samples t-test example from the text, a psychologist compared calorie estimates of regular junk food eaters (mean 168.12) and rare junk food eaters (mean 220.71). The two-tailed p-value was .015. What was the correct conclusion?
An exercise asks you to consider a study where the correlation between height and IQ is +0.13 in a sample of 35. For a two-tailed test with this sample size, the critical r-value is 0.334. Is this result statistically significant?
In a sample of 88 students, the correlation between feelings of disgust and the harshness of moral judgments was +0.23. The two-tailed critical r-value for a sample of 90 is 0.207. What should be concluded?
An exercise describes a sample of 25 students who rated their friendliness, yielding a mean of 5.30 and a standard deviation of 1.50. To test if this is different from an average rating of 4, what is the first step in conducting the one-sample t-test?
What is the common guideline for an adequate level of statistical power that researchers should aim for before collecting data?
What is meant by the term 'Bayesian statistics' as a potential alternative to null hypothesis testing?
The decision to reject or retain the null hypothesis is not guaranteed to be correct. What kind of decision error has been made if the null hypothesis is true, but the researcher rejects it?
According to the text, the one-sample t-test is used for comparing one sample mean with a hypothetical population mean, while the dependent-samples t-test is used for what purpose?
Which of the following is an example of an open science practice encouraged as a response to the 'replicability crisis'?
The text states that the logic of null hypothesis testing involves assuming the null hypothesis is true and then making a decision. If the sample result would be unlikely under this assumption, what is the appropriate decision?
When comparing more than two means, why is using an ANOVA preferable to conducting multiple t-tests?